# coding=utf-8
# Copyright 2022 The HuggingFace Inc. team. All rights reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
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#     http://www.apache.org/licenses/LICENSE-2.0
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# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
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""" Testing suite for the PyTorch ViT Hybrid model. """


import unittest

from transformers import ViTHybridConfig
from transformers.testing_utils import is_flaky, require_accelerate, require_torch, require_vision, slow, torch_device
from transformers.utils import cached_property, is_torch_available, is_vision_available

from ...test_configuration_common import ConfigTester
from ...test_modeling_common import ModelTesterMixin, _config_zero_init, floats_tensor, ids_tensor
from ...test_pipeline_mixin import PipelineTesterMixin


if is_torch_available():
    import torch
    from torch import nn

    from transformers import ViTHybridForImageClassification, ViTHybridImageProcessor, ViTHybridModel


if is_vision_available():
    from PIL import Image


class ViTHybridModelTester:
    def __init__(
        self,
        parent,
        batch_size=13,
        image_size=64,
        patch_size=2,
        num_channels=3,
        is_training=True,
        use_labels=True,
        hidden_size=32,
        num_hidden_layers=2,
        num_attention_heads=4,
        intermediate_size=37,
        hidden_act="gelu",
        hidden_dropout_prob=0.1,
        attention_probs_dropout_prob=0.1,
        type_sequence_label_size=10,
        initializer_range=0.02,
        backbone_featmap_shape=[1, 16, 4, 4],
        scope=None,
    ):
        self.parent = parent
        self.batch_size = batch_size
        self.image_size = image_size
        self.patch_size = patch_size
        self.num_channels = num_channels
        self.is_training = is_training
        self.use_labels = use_labels
        self.hidden_size = hidden_size
        self.num_hidden_layers = num_hidden_layers
        self.num_attention_heads = num_attention_heads
        self.intermediate_size = intermediate_size
        self.hidden_act = hidden_act
        self.hidden_dropout_prob = hidden_dropout_prob
        self.attention_probs_dropout_prob = attention_probs_dropout_prob
        self.type_sequence_label_size = type_sequence_label_size
        self.initializer_range = initializer_range
        self.scope = scope
        self.backbone_featmap_shape = backbone_featmap_shape

        # in ViT hybrid, the seq length equals the number of patches + 1 (we add 1 for the [CLS] token)
        # the number of patches is based on the feature map of the backbone, which by default uses an output stride
        # of 32, which means that the feature map has a spatial resolution of 1/32 of the input image size
        num_patches = (self.image_size // 32) ** 2
        self.seq_length = num_patches + 1

    def prepare_config_and_inputs(self):
        pixel_values = floats_tensor([self.batch_size, self.num_channels, self.image_size, self.image_size])

        labels = None
        if self.use_labels:
            labels = ids_tensor([self.batch_size], self.type_sequence_label_size)

        config = self.get_config()

        return config, pixel_values, labels

    def get_config(self):
        backbone_config = {
            "global_padding": "same",
            "layer_type": "bottleneck",
            "depths": [3, 4, 9],
            "out_features": ["stage1", "stage2", "stage3"],
            "embedding_dynamic_padding": True,
            "hidden_sizes": [4, 8, 16, 32],
            "num_groups": 2,
        }

        return ViTHybridConfig(
            image_size=self.image_size,
            patch_size=self.patch_size,
            num_channels=self.num_channels,
            hidden_size=self.hidden_size,
            num_hidden_layers=self.num_hidden_layers,
            num_attention_heads=self.num_attention_heads,
            intermediate_size=self.intermediate_size,
            hidden_act=self.hidden_act,
            hidden_dropout_prob=self.hidden_dropout_prob,
            attention_probs_dropout_prob=self.attention_probs_dropout_prob,
            is_decoder=False,
            initializer_range=self.initializer_range,
            backbone_featmap_shape=self.backbone_featmap_shape,
            backbone_config=backbone_config,
            backbone=None,
        )

    def create_and_check_model(self, config, pixel_values, labels):
        model = ViTHybridModel(config=config)
        model.to(torch_device)
        model.eval()
        result = model(pixel_values)
        self.parent.assertEqual(result.last_hidden_state.shape, (self.batch_size, self.seq_length, self.hidden_size))

    def create_and_check_for_image_classification(self, config, pixel_values, labels):
        config.num_labels = self.type_sequence_label_size
        model = ViTHybridForImageClassification(config)
        model.to(torch_device)
        model.eval()
        result = model(pixel_values, labels=labels)
        self.parent.assertEqual(result.logits.shape, (self.batch_size, self.type_sequence_label_size))

    def prepare_config_and_inputs_for_common(self):
        config_and_inputs = self.prepare_config_and_inputs()
        config, pixel_values, labels = config_and_inputs
        inputs_dict = {"pixel_values": pixel_values}
        return config, inputs_dict


@require_torch
class ViTHybridModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCase):
    """
    Here we also overwrite some of the tests of test_modeling_common.py, as ViT does not use input_ids, inputs_embeds,
    attention_mask and seq_length.
    """

    all_model_classes = (ViTHybridModel, ViTHybridForImageClassification) if is_torch_available() else ()
    pipeline_model_mapping = (
        {"image-feature-extraction": ViTHybridModel, "image-classification": ViTHybridForImageClassification}
        if is_torch_available()
        else {}
    )
    test_pruning = False
    test_resize_embeddings = False
    test_head_masking = False
    model_split_percents = [0.5, 0.9]

    def setUp(self):
        self.model_tester = ViTHybridModelTester(self)
        self.config_tester = ConfigTester(self, config_class=ViTHybridConfig, has_text_modality=False, hidden_size=37)

    def test_config(self):
        self.config_tester.run_common_tests()

    @unittest.skip(reason="ViT does not use inputs_embeds")
    def test_inputs_embeds(self):
        pass

    def test_model_common_attributes(self):
        config, _ = self.model_tester.prepare_config_and_inputs_for_common()

        for model_class in self.all_model_classes:
            model = model_class(config)
            self.assertIsInstance(model.get_input_embeddings(), (nn.Module))
            x = model.get_output_embeddings()
            self.assertTrue(x is None or isinstance(x, nn.Linear))

    def test_model(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_model(*config_and_inputs)

    def test_for_image_classification(self):
        config_and_inputs = self.model_tester.prepare_config_and_inputs()
        self.model_tester.create_and_check_for_image_classification(*config_and_inputs)

    def test_initialization(self):
        config, inputs_dict = self.model_tester.prepare_config_and_inputs_for_common()

        configs_no_init = _config_zero_init(config)
        for model_class in self.all_model_classes:
            model = model_class(config=configs_no_init)
            # Skip the check for the backbone
            for name, module in model.named_modules():
                if module.__class__.__name__ == "ViTHybridPatchEmbeddings":
                    backbone_params = [f"{name}.{key}" for key in module.state_dict().keys()]
                    break

            for name, param in model.named_parameters():
                if param.requires_grad:
                    if name in backbone_params:
                        continue
                    self.assertIn(
                        ((param.data.mean() * 1e9).round() / 1e9).item(),
                        [0.0, 1.0],
                        msg=f"Parameter {name} of model {model_class} seems not properly initialized",
                    )

    @slow
    def test_model_from_pretrained(self):
        model_name = "google/vit-hybrid-base-bit-384"
        model = ViTHybridModel.from_pretrained(model_name)
        self.assertIsNotNone(model)

    @is_flaky(description="is_flaky https://github.com/huggingface/transformers/issues/29516")
    def test_batching_equivalence(self):
        super().test_batching_equivalence()


# We will verify our results on an image of cute cats
def prepare_img():
    image = Image.open("./tests/fixtures/tests_samples/COCO/000000039769.png")
    return image


@require_torch
@require_vision
class ViTModelIntegrationTest(unittest.TestCase):
    @cached_property
    def default_image_processor(self):
        return (
            ViTHybridImageProcessor.from_pretrained("google/vit-hybrid-base-bit-384")
            if is_vision_available()
            else None
        )

    @slow
    def test_inference_image_classification_head(self):
        model = ViTHybridForImageClassification.from_pretrained("google/vit-hybrid-base-bit-384").to(torch_device)

        image_processor = self.default_image_processor
        image = prepare_img()
        inputs = image_processor(images=image, return_tensors="pt").to(torch_device)

        # forward pass
        with torch.no_grad():
            outputs = model(**inputs)

        # verify the logits
        expected_shape = torch.Size((1, 1000))
        self.assertEqual(outputs.logits.shape, expected_shape)

        expected_slice = torch.tensor([-1.9090, -0.4993, -0.2389]).to(torch_device)

        self.assertTrue(torch.allclose(outputs.logits[0, :3], expected_slice, atol=1e-4))

    @slow
    @require_accelerate
    def test_accelerate_inference(self):
        image_processor = ViTHybridImageProcessor.from_pretrained("google/vit-hybrid-base-bit-384")
        model = ViTHybridForImageClassification.from_pretrained("google/vit-hybrid-base-bit-384", device_map="auto")

        image = prepare_img()

        inputs = image_processor(images=image, return_tensors="pt").to(torch_device)
        outputs = model(**inputs)
        logits = outputs.logits
        # model predicts one of the 1000 ImageNet classes
        predicted_class_idx = logits.argmax(-1).item()

        self.assertTrue(model.config.id2label[predicted_class_idx], "tabby, tabby cat")
